CHAPTER 3 Forecasting
FORECAST: A statement about the future value of a variable of interest such as demand. Forecasts affect decisions and activities throughout an organization Accounting, finance Human resources Marketing MIS Operations Product / service design
Uses of Forecasts Accounting Cost/profit estimates Finance Cash flow and funding Human Resources Hiring/recruiting/training Marketing Pricing, promotion, strategy MIS IT/IS systems, services Operations Schedules, MRP, workloads Product/service design New products and services
Assumes causal system past ==> future Forecasts rarely perfect because of randomness Forecasts more accurate for groups vs. individuals Forecast accuracy decreases as time horizon increases I see that you will get an A this semester.
Elements of a Good Forecast Timely Accurate Reliable Meaningful Written Easy to use
Steps in the Forecasting Process Step 1 Determine purpose of forecast Step 2 Establish a time horizon Step 3 Select a forecasting technique Step 4 Gather and analyze data Step 5 Prepare the forecast Step 6 Monitor the forecast “The forecast”
Types of Forecasts Judgmental - uses subjective inputs Time series - uses historical data assuming the future will be like the past Associative models - uses explanatory variables to predict the future
Judgmental Forecasts Executive opinions Sales force opinions Consumer surveys Outside opinion Delphi method Opinions of managers and staff Achieves a consensus forecast
Time Series Forecasts Trend - long-term movement in data Seasonality - short-term regular variations in data Cycle – wavelike variations of more than one year’s duration Irregular variations - caused by unusual circumstances Random variations - caused by chance
Forecast Variations Figure 3.1 Trend Cycles Irregular variation 90 89 88 Seasonal variations
week.... Now, next week we should sell.... Naive Forecasts Uh, give me a minute.... We sold 250 wheels last week.... Now, next week we should sell.... The forecast for any period equals the previous period’s actual value.
Techniques for Averaging Moving average Weighted moving average Exponential smoothing
MAn = n Ai Moving Averages Moving average – A technique that averages a number of recent actual values, updated as new values become available. Weighted moving average – More recent values in a series are given more weight in computing the forecast. MAn = n Ai i = 1
Simple Moving Average Actual MA5 MA3 MAn = n Ai i = 1
Exponential Smoothing Ft = Ft-1 + (At-1 - Ft-1) Premise--The most recent observations might have the highest predictive value. Therefore, we should give more weight to the more recent time periods when forecasting.
Exponential Smoothing Ft = Ft-1 + (At-1 - Ft-1) Weighted averaging method based on previous forecast plus a percentage of the forecast error A-F is the error term, is the % feedback
Example 3 - Exponential Smoothing
Picking a Smoothing Constant .1 .4 Actual
Associative Forecasting Predictor variables - used to predict values of variable interest Regression - technique for fitting a line to a set of points Least squares line - minimizes sum of squared deviations around the line
Linear Model Seems Reasonable Computed relationship A straight line is fitted to a set of sample points.
Forecast Accuracy Error - difference between actual value and predicted value Mean Absolute Deviation (MAD) Average absolute error Mean Squared Error (MSE) Average of squared error Mean Absolute Percent Error (MAPE) Average absolute percent error
MAD, MSE, and MAPE Actual forecast MAD = n MSE = Actual forecast) - 1 2 n ( MAPE = Actual forecast n / Actual*100) (
Example 10
Choosing a Forecasting Technique No single technique works in every situation Two most important factors Cost Accuracy Other factors include the availability of: Historical data Computers Time needed to gather and analyze the data Forecast horizon
Exponential Smoothing
Linear Trend Equation
Simple Linear Regression
United Airlines example Workload/Scheduling SSU9 United Airlines example